https://github.com/Dongvdong/v1_1_slam_tool
#!/usr/bin/python
# -*- coding: UTF-8 -*-
import math
from pyecharts import Line3D
from pyecharts import Line
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import axes3d
import numpy as np
# matplotlib画一条单独的3D轨迹
def Draw_trace1(x,y,z):
fig = plt.figure()
ax = fig.gca(projection='3d')
ax.set_title("3D_Curve")
ax.set_xlabel("x(m)")
ax.set_ylabel("y(m)")
ax.set_zlabel("z(m)")
figure = ax.plot(x, y, z, c='r')
plt.show()
# matplotlib将多条3D轨迹画在同一个图里
def API_txt_to_Draw3D(list_name_xyz):
x_list=[]
y_list=[]
z_list=[]
for data_i in list_name_xyz:
nam_i=data_i[0]
x_i=float(data_i[1])
y_i=float(data_i[2])
z_i=float(data_i[3])
x_list.append(x_i)
y_list.append(y_i)
z_list.append(z_i)
return x_list,y_list,z_list
def Draw3D_trace_more(tracelist):
fig = plt.figure()
ax = fig.gca(projection='3d')
#ax = fig.add_subplot(projection='3d')
#ax.set_zlim3d(zmin=-2, zmax=2) #纵轴范围
#plt.xlim(-1500,100)
#plt.ylim(-50,50)
#ax.set_title("3D_Curve")
ax.set_xlabel("x(m)")
ax.set_ylabel("y(m)")
ax.set_zlabel("z(m)")
ax.set_aspect("auto")#设置x,y z轴等比例
color=["blue","green","red","orange","purple","pink","yellow"]
linestyle=["-","--","-.","-"] #":"
for i in range(0,len(tracelist)):
#print(tracelist[i])
xi_list,yi_list,zi_list=API_txt_to_Draw3D(tracelist[i])
color_i=color[i]
figure = ax.plot(xi_list, yi_list, zi_list, c=color_i,linestyle=linestyle[i])
plt.grid()#网格线
plt.show()
#画单个2维平面轨迹
def Draw2D_trace_gpsvreal(x1,y1,x2,y2):
#绘画轨迹
print("画轨迹图")
plt.title('Error mapped onto trajectory')
plt.ylabel('Y(m)')
plt.xlabel('X(m)')
#plt.annotate('blue ', xy=(2,5), xytext=(2, 10),arrowprops=dict(facecolor='black', shrink=0.01),)
#x=[1, 2, 3, 4,3,2,1]
#y=[1, 4, 9, 16,3,5,6]
plt.plot(x1,y1,color='b',linestyle='dashed')
'''
color：线条颜色，值r表示红色（red）
marker：点的形状，值o表示点为圆圈标记（circle marker）
linestyle：线条的形状，值dashed表示用虚线连接各点
'''
#x=[3, 4, 5, 6]
#y=[1, 4, 9, 16]
#plt.plot(x2, y2, color='r',marker='o',linestyle='dashed')
plt.plot(x2, y2, color='r')
'''
axis：坐标轴范围
语法为axis[xmin, xmax, ymin, ymax]，
也就是axis[x轴最小值, x轴最大值, y轴最小值, y轴最大值]
'''
#plt.axis([0, 6, 0, 20])
plt.grid()#网格线
#plt.grid(axis='x',color = 'r', linestyle = '--', linewidth = 0.5)## 设置 y 就在轴方向显示网格线
plt.show()
#画3个对对比 2维平面轨迹
"""
输入 data_list 多个轨迹的enu列表
要画的x轴 xlabel='X(m)' "Y(m)" "Z(m)"
要画的y轴 ylabeln='X(m)' "Y(m)" "Z(m)"
"""
def Draw2D_trace_gpsvreal_list(data_list,xlabel='X(m)',ylabeln='Y(m)'):
color=["blue","green","red","orange","purple","pink","yellow"]
'''
'-' solid line style
'--' dashed line style
'-.' dash-dot line style
':' dotted line style
'''
linestyle=["-","--","-.","-"] #:
xmax=0
xmin=0
ymax=0
ymin=0
for i in range(0,len(data_list)):
linexylist=data_list[i]
linexlist=[]
lineylist=[]
#根据轴 决定画的是侧视图还是俯视图
if xlabel=="X(m)":
linexlist=linexylist[0]
elif xlabel=="Y(m)":
linexlist=linexylist[1]
elif xlabel=="Z(m)":
linexlist=linexylist[2]
if ylabeln=="X(m)":
lineylist=linexylist[0]
elif ylabeln=="Y(m)":
lineylist=linexylist[1]
elif ylabeln=="Z(m)":
lineylist=linexylist[2]
threah=0
#if xlabel=="X(m)" and ylabeln=="Y(m)":
#threah=100
# xmax=int(max(linexlist)+threah)
# xmin=int(min(linexlist)-threah)
# ymax=int(max(lineylist)+threah)
# ymin=int(min(lineylist)-threah)
#绘画轨迹
#print("画轨迹图")
#plt.title('Error mapped onto trajectory')
plt.xlabel(xlabel)
plt.ylabel(ylabeln)
#plt.annotate('blue ', xy=(2,5), xytext=(2, 10),arrowprops=dict(facecolor='black', shrink=0.01),)
#x=[1, 2, 3, 4,3,2,1]
#y=[1, 4, 9, 16,3,5,6]
color_i=color[i]
line_i=linestyle[i]
plt.plot(linexlist,lineylist,color=color_i,linestyle=line_i)
#plt.xlim(xmin,xmax)
#plt.ylim(ymin,ymax)
plt.grid()#网格线
plt.show()
'''
color：线条颜色，值r表示红色（red）
cnames = {
'aliceblue': '#F0F8FF',
'antiquewhite': '#FAEBD7',
'aqua': '#00FFFF',
'aquamarine': '#7FFFD4',
'azure': '#F0FFFF',
'beige': '#F5F5DC',
'bisque': '#FFE4C4',
'black': '#000000',
'blanchedalmond': '#FFEBCD',
'blue': '#0000FF',
'blueviolet': '#8A2BE2',
'brown': '#A52A2A',
'burlywood': '#DEB887',
'cadetblue': '#5F9EA0',
'chartreuse': '#7FFF00',
'chocolate': '#D2691E',
'coral': '#FF7F50',
'cornflowerblue': '#6495ED',
'cornsilk': '#FFF8DC',
'crimson': '#DC143C',
'cyan': '#00FFFF',
'darkblue': '#00008B',
'darkcyan': '#008B8B',
'darkgoldenrod': '#B8860B',
'darkgray': '#A9A9A9',
'darkgreen': '#006400',
'darkkhaki': '#BDB76B',
'darkmagenta': '#8B008B',
'darkolivegreen': '#556B2F',
'darkorange': '#FF8C00',
'darkorchid': '#9932CC',
'darkred': '#8B0000',
'darksalmon': '#E9967A',
'darkseagreen': '#8FBC8F',
'darkslateblue': '#483D8B',
'darkslategray': '#2F4F4F',
'darkturquoise': '#00CED1',
'darkviolet': '#9400D3',
'deeppink': '#FF1493',
'deepskyblue': '#00BFFF',
'dimgray': '#696969',
'dodgerblue': '#1E90FF',
'firebrick': '#B22222',
'floralwhite': '#FFFAF0',
'forestgreen': '#228B22',
'fuchsia': '#FF00FF',
'gainsboro': '#DCDCDC',
'ghostwhite': '#F8F8FF',
'gold': '#FFD700',
'goldenrod': '#DAA520',
'gray': '#808080',
'green': '#008000',
'greenyellow': '#ADFF2F',
'honeydew': '#F0FFF0',
'hotpink': '#FF69B4',
'indianred': '#CD5C5C',
'indigo': '#4B0082',
'ivory': '#FFFFF0',
'khaki': '#F0E68C',
'lavender': '#E6E6FA',
'lavenderblush': '#FFF0F5',
'lawngreen': '#7CFC00',
'lemonchiffon': '#FFFACD',
'lightblue': '#ADD8E6',
'lightcoral': '#F08080',
'lightcyan': '#E0FFFF',
'lightgoldenrodyellow': '#FAFAD2',
'lightgreen': '#90EE90',
'lightgray': '#D3D3D3',
'lightpink': '#FFB6C1',
'lightsalmon': '#FFA07A',
'lightseagreen': '#20B2AA',
'lightskyblue': '#87CEFA',
'lightslategray': '#778899',
'lightsteelblue': '#B0C4DE',
'lightyellow': '#FFFFE0',
'lime': '#00FF00',
'limegreen': '#32CD32',
'linen': '#FAF0E6',
'magenta': '#FF00FF',
'maroon': '#800000',
'mediumaquamarine': '#66CDAA',
'mediumblue': '#0000CD',
'mediumorchid': '#BA55D3',
'mediumpurple': '#9370DB',
'mediumseagreen': '#3CB371',
'mediumslateblue': '#7B68EE',
'mediumspringgreen': '#00FA9A',
'mediumturquoise': '#48D1CC',
'mediumvioletred': '#C71585',
'midnightblue': '#191970',
'mintcream': '#F5FFFA',
'mistyrose': '#FFE4E1',
'moccasin': '#FFE4B5',
'navajowhite': '#FFDEAD',
'navy': '#000080',
'oldlace': '#FDF5E6',
'olive': '#808000',
'olivedrab': '#6B8E23',
'orange': '#FFA500',
'orangered': '#FF4500',
'orchid': '#DA70D6',
'palegoldenrod': '#EEE8AA',
'palegreen': '#98FB98',
'paleturquoise': '#AFEEEE',
'palevioletred': '#DB7093',
'papayawhip': '#FFEFD5',
'peachpuff': '#FFDAB9',
'peru': '#CD853F',
'pink': '#FFC0CB',
'plum': '#DDA0DD',
'powderblue': '#B0E0E6',
'purple': '#800080',
'red': '#FF0000',
'rosybrown': '#BC8F8F',
'royalblue': '#4169E1',
'saddlebrown': '#8B4513',
'salmon': '#FA8072',
'sandybrown': '#FAA460',
'seagreen': '#2E8B57',
'seashell': '#FFF5EE',
'sienna': '#A0522D',
'silver': '#C0C0C0',
'skyblue': '#87CEEB',
'slateblue': '#6A5ACD',
'slategray': '#708090',
'snow': '#FFFAFA',
'springgreen': '#00FF7F',
'steelblue': '#4682B4',
'tan': '#D2B48C',
'teal': '#008080',
'thistle': '#D8BFD8',
'tomato': '#FF6347',
'turquoise': '#40E0D0',
'violet': '#EE82EE',
'wheat': '#F5DEB3',
'white': '#FFFFFF',
'whitesmoke': '#F5F5F5',
'yellow': '#FFFF00',
'yellowgreen': '#9ACD32'}
marker：点的形状，值o表示点为圆圈标记（circle marker）
'.' point marker
',' pixel marker
'o' circle marker
'v' triangle_down marker
'^' triangle_up marker
'<' triangle_left marker
'>' triangle_right marker
'1' tri_down marker
'2' tri_up marker
'3' tri_left marker
'4' tri_right marker
's' square marker
'p' pentagon marker
'*' star marker
'h' hexagon1 marker
'H' hexagon2 marker
'+' plus marker
'x' x marker
'D' diamond marker
'd' thin_diamond marker
'|' vline marker
'_' hline marker
linestyle：线条的形状，值dashed表示用虚线连接各点
'-' solid line style
'--' dashed line style
'-.' dash-dot line style
':' dotted line style
'''
#画单个折线图 水平误差折线
def Draw2D(realgps_enu_listx,realgps_enu_listy,slamgps_enu_listx,slamgps_enu_listy):
line = Line("真值和定位轨迹")
v_x = [5, 20, 36, 10, 10, 100]
v_y = [55, 60, 16, 20, 15, 80]
line.add("真值",
realgps_enu_listx,
realgps_enu_listy,
#mark_point=["average"]
)
line.add(
"SLAM",
slamgps_enu_listx,
slamgps_enu_listy,
#mark_line=[“average”] 表示补充一条平均值线
mark_line=["average", "max", "min"],
#mark_point=["average", "max", "min"],
#mark_point_symbol="diamond",
#mark_point_textcolor="#40ff27",
is_smooth=True,
)
line.render()
#画多个折线图 在同一个图里 水平误差折线
def Draw2D_error_onePic(slam_enu_error_list_xyz):
# 第一个为真值
# len(gps_data_list)和 len(slam_data_list)数量应该是对等的
error_list=[]
hengzuobiao=[]
fps=20.0
time_interbval=1.0/fps
for i in range(0,len(slam_enu_error_list_xyz)):
slam_enu_error_i=slam_enu_error_list_xyz[i]
#line = Line("Positioning Error (m) "+ str(i))
error_x_list=slam_enu_error_i[0]
error_y_list=slam_enu_error_i[1]
error_z_list=slam_enu_error_i[2]
for j in range(0,len(error_x_list)):
hengzuobiao.append((j*time_interbval))
plt.figure(figsize=(20, 10), dpi=100)
plt.plot(hengzuobiao, error_x_list, c='red', label="x")
plt.plot(hengzuobiao, error_y_list, c='green', linestyle='--', label="y")
plt.plot(hengzuobiao, error_z_list, c='blue', linestyle='-.', label="z")
plt.scatter(hengzuobiao, error_x_list, c='red')
plt.scatter(hengzuobiao, error_y_list, c='green')
plt.scatter(hengzuobiao, error_z_list, c='blue')
plt.legend(loc='best')
#plt.yticks(range(0, 50, 5))
plt.grid(True, linestyle='--', alpha=0.5)
plt.xlabel("Time (s)", fontdict={'size': 16})
plt.ylabel("Error (m)", fontdict={'size': 16})
plt.title("Position Error", fontdict={'size': 20})
plt.show()
#画单组 3个折线图 各自单独一个图里 水平误差折线
def Draw2D_error_MorePic(slam_enu_error_list_xyz):
# 第一个为真值
# len(gps_data_list)和 len(slam_data_list)数量应该是对等的
fps=10.0
time_interbval=1.0/fps
plt.figure()
'''
num = None, # 设定figure名称。系统默认按数字升序命名的figure_num（透视表输出窗口）e.g. “figure1”。可自行设定figure名称，名称或是INT，或是str类型；
figsize=None, # 设定figure尺寸。系统默认命令是rcParams["figure.fig.size"] = [6.4, 4.8]，即figure长宽为6.4 * 4.8；
dpi=None, # 设定figure像素密度。系统默命令是rcParams["sigure.dpi"] = 100；
facecolor=None, # 设定figure背景色。系统默认命令是rcParams["figure.facecolor"] = 'w'，即白色white；
edgecolor=None, frameon=True, # 设定要不要绘制轮廓&轮廓颜色。系统默认绘制轮廓，轮廓染色rcParams["figure.edgecolor"]='w',即白色white；
FigureClass=<class 'matplotlib.figure.Figure'>, # 设定使不使用一个figure模板。系统默认不使用；
'''
#fig, axs = plt.subplots(nrows=3, ncols=1)#3行1列 figsize=(20, 6) dpi=100 像素密度
colors = ['red', 'green', 'blue']
line_style = ['-', '--', '-.']
y_labels = ["x Error (m)", "y Error (m)", "z Error (m)"] #Error (m)
slam_enu_error_i=slam_enu_error_list_xyz
error_x_list=slam_enu_error_i[0]
error_y_list=slam_enu_error_i[1]
error_z_list=slam_enu_error_i[2]
x_i=[]
for j in range(0,len(error_x_list)):
x_i.append(j)
x_data=x_i
y_data = [error_x_list, error_y_list, error_z_list]
# xmin=int(min(x_data))
# xmax=int(max(x_data))
# ymin=int(min(y_data))
# ymax=int(max(y_data))
# yth=2
for i in range(1,4):
plt.subplot(3,1,i)
#plt.plot([0,1],[0,1])
i=i-1
# axs[i].plot(x_data, y_data[i], c=colors[i], label=y_labels[i], linestyle=line_style[i],linewidth=1,marker='.'
# ,markeredgecolor=colors[i],markersize='1',markeredgewidth=1) #label='total' alpha=0.5
plt.plot(x_data, y_data[i], c=colors[i], label=y_labels[i], linestyle=line_style[i],linewidth=1,marker='.',markersize=0.1)
#plt.scatter(x_data, y_data[i], c=colors[i])
plt.xlabel("ID")
plt.ylabel(y_labels[i])
#axs[i].legend(loc='best') #默认: 对于Axes， ‘best’, 对于Figure, 'upper right'
#axs[i].set_aspect(aspect=0.1)#y轴的单位刻度显示长度 与 x轴的单位刻度显示长度 的比例
#axs[i].axis('equal')#设置y轴和x轴的单位刻度显示长度相同
#axs[i].set_yticks(range(ymin,ymax, 2)) # y轴的间距和显示范围
plt.grid(True, linestyle='--', alpha=0.5)#背景网格线
#plt.set_xlabel("Time (s)", fontdict={'size': 10})#x轴坐标名字 和 字号
#plt.set_ylabel(y_labels[i], fontdict={'size': 10}, rotation=90) #y轴坐标名字 和 字号
#plt.set_title("Position {}".format(y_labels[i]), fontdict={'size': 12}) #标题
#fig.autofmt_xdate()
plt.show()
#画单组 3个折线图 各自单独一个图里 水平误差折线
def Draw2D_error_MorePicv2(slam_enu_error_list_xyz):
# 第一个为真值
# len(gps_data_list)和 len(slam_data_list)数量应该是对等的
fps=10.0
time_interbval=1.0/fps
'''
num = None, # 设定figure名称。系统默认按数字升序命名的figure_num（透视表输出窗口）e.g. “figure1”。可自行设定figure名称，名称或是INT，或是str类型；
figsize=None, # 设定figure尺寸。系统默认命令是rcParams["figure.fig.size"] = [6.4, 4.8]，即figure长宽为6.4 * 4.8；
dpi=None, # 设定figure像素密度。系统默命令是rcParams["sigure.dpi"] = 100；
facecolor=None, # 设定figure背景色。系统默认命令是rcParams["figure.facecolor"] = 'w'，即白色white；
edgecolor=None, frameon=True, # 设定要不要绘制轮廓&轮廓颜色。系统默认绘制轮廓，轮廓染色rcParams["figure.edgecolor"]='w',即白色white；
FigureClass=<class 'matplotlib.figure.Figure'>, # 设定使不使用一个figure模板。系统默认不使用；
'''
fig, axs = plt.subplots(nrows=3, ncols=1)#3行1列 figsize=(20, 6) dpi=100 像素密度
colors = ['red', 'green', 'blue']
line_style = ['-', '--', '-.']
y_labels = ["x Error (m)", "y Error (m)", "z Error (m)"] #Error (m)
slam_enu_error_i=slam_enu_error_list_xyz
error_x_list=slam_enu_error_i[0]
error_y_list=slam_enu_error_i[1]
error_z_list=slam_enu_error_i[2]
x_i=[]
for j in range(0,len(error_x_list)):
x_i.append((j*time_interbval))
x_data=x_i
y_data = [error_x_list, error_y_list, error_z_list]
# xmin=int(min(x_data))
# xmax=int(max(x_data))
# ymin=int(min(y_data))
# ymax=int(max(y_data))
# yth=2
for i in range(3):
# axs[i].plot(x_data, y_data[i], c=colors[i], label=y_labels[i], linestyle=line_style[i],linewidth=1,marker='.'
# ,markeredgecolor=colors[i],markersize='1',markeredgewidth=1) #label='total' alpha=0.5
axs[i].plot(x_data, y_data[i], c=colors[i], label=y_labels[i], linestyle=line_style[i],linewidth=1,marker='.',markersize=0.1)
#axs[i].scatter(x_data, y_data[i], c=colors[i])
#axs[i].legend(loc='best') #默认: 对于Axes， ‘best’, 对于Figure, 'upper right'
#axs[i].set_aspect(aspect=0.1)#y轴的单位刻度显示长度 与 x轴的单位刻度显示长度 的比例
#axs[i].axis('equal')#设置y轴和x轴的单位刻度显示长度相同
#axs[i].set_yticks(range(ymin,ymax, 2)) # y轴的间距和显示范围
axs[i].grid(True, linestyle='--', alpha=0.5)#背景网格线
axs[i].set_xlabel("Time (s)", fontdict={'size': 10})#x轴坐标名字 和 字号
axs[i].set_ylabel(y_labels[i], fontdict={'size': 10}, rotation=90) #y轴坐标名字 和 字号
axs[i].set_title("Position {}".format(y_labels[i]), fontdict={'size': 12}) #标题
#fig.autofmt_xdate()
plt.show()
#画多组 多个折线图 各自单独一个图里 水平误差折线
def Draw2D_error_More33Pic(slam_enu_error_list_xyz):
fps=10.0
time_interbval=1.0/fps
'''
num = None, # 设定figure名称。系统默认按数字升序命名的figure_num（透视表输出窗口）e.g. “figure1”。可自行设定figure名称，名称或是INT，或是str类型；
figsize=None, # 设定figure尺寸。系统默认命令是rcParams["figure.fig.size"] = [6.4, 4.8]，即figure长宽为6.4 * 4.8；
dpi=None, # 设定figure像素密度。系统默命令是rcParams["sigure.dpi"] = 100；
facecolor=None, # 设定figure背景色。系统默认命令是rcParams["figure.facecolor"] = 'w'，即白色white；
edgecolor=None, frameon=True, # 设定要不要绘制轮廓&轮廓颜色。系统默认绘制轮廓，轮廓染色rcParams["figure.edgecolor"]='w',即白色white；
FigureClass=<class 'matplotlib.figure.Figure'>, # 设定使不使用一个figure模板。系统默认不使用；
'''
fig, axs = plt.subplots(nrows=3, ncols=1,dpi=100)#3行1列 figsize=(20, 6) dpi=100 像素密度
#colors = ['blue','green','red']
colors = ['blue','green','red']
line_style = ['-', '--', '-.']
#datalabls=["nogps-nogps","gps-nogps","gps-gps"]
datalabls=["true","vio + gps","vio"]
y_labels = ["x Error (m)", "y Error (m)", "z Error (m)"] #Error (m)
for i in range(0,len(slam_enu_error_list_xyz)):
slam_enu_error_i=slam_enu_error_list_xyz[i]
error_x_list=slam_enu_error_i[0]
error_y_list=slam_enu_error_i[1]
error_z_list=slam_enu_error_i[2]
x_i=[]
for j in range(0,len(error_x_list)):
x_i.append((j*time_interbval))
x_data=x_i
y_data = [error_x_list, error_y_list, error_z_list]
print("数据点总共",len(x_i))
for ii in range(0,3):
# axs[i].plot(x_data, y_data[i], c=colors[i], label=y_labels[i], linestyle=line_style[i],linewidth=1,marker='.'
# ,markeredgecolor=colors[i],markersize='1',markeredgewidth=1) #label='total' alpha=0.5
#添加lable标签显示
#axs[ii].plot(x_data, y_data[ii], c=colors[i], label=datalabls[i], linestyle=line_style[i],linewidth=1,marker='.',markersize=0.1)
axs[ii].plot(x_data, y_data[ii], c=colors[i], linestyle=line_style[i],linewidth=1,marker='.',markersize=0.1)
#axs[ii].scatter(x_data, y_data[ii], c=colors[i])
axs[ii].legend(loc='best')
#axs[ii].gca().set_box_aspect((3, 5, 2)) # 当x、y、z轴范围之比为3:5:2时。
#axs[ii].set_yticks(range(-4, 4, 2))
#if ii==2:
#axs[ii].set_yticks(range(-4, 8, 2))
# if ii==0:
# axs[ii].set_yticks(range(-60, 30, 10))
# elif ii==1:
# axs[ii].set_yticks(range(-30, 70, 20)) # y轴的间距和显示范围
# else:
# axs[ii].set_yticks(range(-20, 30, 10))
axs[ii].grid(True, linestyle='--', alpha=0.5)#背景网格线
axs[ii].set_xlabel("Time (s)", fontdict={'size': 10})#x轴坐标名字 和 字号
axs[ii].set_ylabel(y_labels[ii], fontdict={'size': 10}, rotation=90) #y轴坐标名字 和 字号 Y轴说明是否旋转90度
axs[ii].set_title("Position {}".format(y_labels[ii]), fontdict={'size': 12}) #标题
fig.autofmt_xdate()
plt.show()
def API_Cal_wucha(errorlist_txyz0_error):
# 0 累计误差 计算总体转化均方根误差
error_all=0
for i in range(0,len(errorlist_txyz0_error)):
#print(errorlist_txyz0_error[i])
#distance=sqrt((errorlist_txyz0_error[i][0])**2+(errorlist_txyz0_error[i][1])**2)
#
distance=sqrt((errorlist_txyz0_error[i][0])**2+(errorlist_txyz0_error[i][1])**2+(errorlist_txyz0_error[i][2])**2)
#print(i,time_realgps_slamgps_list[i])
error_all=error_all+distance
error_averrange=sqrt(error_all/len(errorlist_txyz0_error))
return error_averrange
if __name__ == "__main__":
from API_0File_Read_Write import *
data_list=[] #文件名
data_list.append("data/1GNSS_from_img.txt")
#data_list.append("data/2ENU_from_GNSS.txt")
data_list.append("data/3GNSS_From_ENU.txt")
draw_tracelist=[]# 存放多组要绘画的3D图数据 分列存放
for txt_name_i in data_list:
list_name_xyz = API_read2txt(txt_name_i)
draw_tracelist.append(list_name_xyz)
Draw3D_trace_more(draw_tracelist)
'''
#1 gps真值
#enu_realgps_txt_savename0=root_path+"真实gps完整.txt"
enu_realgps_txt_savename0=root_path+"带gps建图带gps定位"+"/匹配enu_real"+slamgps_txt_name
#2 不同状态的定位
enu_slamgps_txt_savename2=root_path+"不带gps建图不带gps定位"+"/匹配enu_slam"+slamgps_txt_name
enu_slamgps_txt_savename1=root_path+"带gps建图不带gps定位"+"/匹配enu_slam"+slamgps_txt_name
enu_slamgps_txt_savename3=root_path+"带gps建图带gps定位"+"/匹配enu_slam"+slamgps_txt_name
data_list=[] #文件名
data_list.append(enu_realgps_txt_savename0)
data_list.append(enu_slamgps_txt_savename1)
data_list.append(enu_slamgps_txt_savename2)
data_list.append(enu_slamgps_txt_savename3)
draw_tracelist=[]# enu数据列表
for i in range(0,len(data_list)):
list_txyz0,list_x0,list_y0,list_z0 = readtxt_ENU(data_list[i])
draw_tracelist.append([list_x0,list_y0,list_z0])
#==========1 画多个3D轨迹在同一个3D图 对比轨迹
Draw3D_trace_more(draw_tracelist)
#==========2 画多个3D轨迹的2D侧视图
Draw2D_trace_gpsvreal_list(draw_tracelist,"X(m)","Y(m)") #-俯视图xy
Draw2D_trace_gpsvreal_list(draw_tracelist,"X(m)","Z(m)") #-左视图xz
Draw2D_trace_gpsvreal_list(draw_tracelist,"Y(m)","Z(m)") #-右视图yz
#============3 画2D折线图误差
#3-1 加载数据 折线误差slam值
SlamEnuError_txtNamelist=[] #保存slam enu error轨迹 文件名
#SlamEnuError_txtNamelist.append(enu_realgps_txt_savename0)
SlamEnuError_txtNamelist.append(enu_slamgps_txt_savename3)
SlamEnuError_txtNamelist.append(enu_slamgps_txt_savename1)
SlamEnuError_txtNamelist.append(enu_slamgps_txt_savename2)
draw_slamEnutraceError_list=[] #保存slam enu轨迹 数据
for i in range(0,len(SlamEnuError_txtNamelist)):
errorlist_txyz0_error,list_x0_error,list_y0_error,list_z0_error = readtxt_ENU_Error(SlamEnuError_txtNamelist[i])
errorlist_txyx0_error_skip=[]
errorlist_txyy0_error_skip=[]
errorlist_txyz0_error_skip=[]
# #步长 3
# for i in range(0,len(errorlist_txyz0_error),100):
# errorlist_txyx0_error_skip.append(list_x0_error[i])
# errorlist_txyy0_error_skip.append(list_y0_error[i])
# errorlist_txyz0_error_skip.append(list_z0_error[i])
draw_slamEnutraceError_list.append([list_x0_error,list_y0_error,list_z0_error])
#draw_slamEnutraceError_list.append([errorlist_txyx0_error_skip,errorlist_txyy0_error_skip,errorlist_txyz0_error_skip])
#=============3-2 画数据 2D折线图画在多个行里
Draw2D_error_More33Pic(draw_slamEnutraceError_list)
'''
标签：plt,figure,list,绘画,slam,可视化,enu,error,data From： https://www.cnblogs.com/gooutlook/p/18253357